EE 126. Probability in Electrical Engineering and Computer Science

Current Schedule (Fall 2016)


Updated Description: (4 units) Three hours of lecture and one hour of discussion per week. This course explains applications of probability in electrical engineering and computer sciences: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. The necessary concepts from basic probability and linear algebra are reviewed.

Prerequisites: CS 70.

Course objectives: This course introduces probability and probabilistic models. The objective is to equip students with the basic tools required to build and analyze such models in both the discrete and continuous context.

Topics Covered:

General Catalog